Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengthy scan times for 3D multiparametric qMRI acquisition limit its clinical utility. Here, we propose SUMMIT, an innovative imaging methodology that includes data acquisition and an unsupervised reconstruction for simultaneous multiparametric qMRI. SUMMIT first encodes multiple important quantitative properties into highly undersampled k-space. It further leverages implicit neural representation incorporated with a dedicated physics model to reconstruct the desired multiparametric maps without needing external training datasets. SUMMIT delivers co-registered T1, T2, T2*, and quantitative susceptibility mapping. Extensive simulations and phantom imaging demonstrate SUMMIT's high accuracy. Additionally, the proposed unsupervised approach for qMRI reconstruction also introduces a novel zero-shot learning paradigm for multiparametric imaging applicable to various medical imaging modalities.

翻译：定量磁共振成像（qMRI）能够提供组织特异性的物理参数，在神经科学研究和临床实践中具有巨大潜力。然而，三维多参数qMRI采集所需的冗长扫描时间限制了其临床应用。本文提出了一种创新的成像方法SUMMIT，该方法包含数据采集和无监督重建，用于实现同步多参数qMRI。SUMMIT首先将多个重要的定量属性编码到高度欠采样的k空间中。进一步，它利用结合专用物理模型的隐式神经表征来重建所需的多参数图，而无需外部训练数据集。SUMMIT能够生成配准良好的T1、T2、T2*及定量磁化率图。大量的仿真与体模成像实验证明了SUMMIT的高精度。此外，所提出的无监督qMRI重建方法也为适用于多种医学成像模式的多参数成像引入了一种新颖的零样本学习范式。